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At least 325 records · Page 18Linked to original sources
PubMed2006

Topographical organization of pathways from somatosensory cortex through the pontine nuclei to tactile regions of the rat cerebellar hemispheres.

The granule cell layer of the cerebellar hemispheres contains a patchy and noncontinuous map of the body surface, consisting of a complex mosaic of multiple perioral tactile representations. Previous physiological studies have shown that cerebrocerebellar mossy fibre projections, conveyed through the pontine nuclei, are mapped in registration with peripheral tactile projections to the cerebellum. In contrast to the fractured cerebellar map, the primary somatosensory cortex (SI) is somatotopically organized. To understand better the map transformation occurring in cerebrocerebellar pathways, we injected axonal tracers in electrophysiologically defined locations in Sprague-Dawley rat folium crus IIa, and mapped the distribution of retrogradely labelled neurons within the pontine nuclei using three-dimensional (3-D) reconstructions. Tracer injections within the large central upper lip patch in crus IIa-labelled neurons located centrally in the pontine nuclei, primarily contralateral to the injected side. Larger injections (covering multiple crus IIa perioral representations) resulted in labelling extending only slightly beyond this region, with a higher density and more ipsilaterally labelled neurons. Combined axonal tracer injections in upper lip representations in SI and crus IIa, revealed a close spatial correspondence between the cerebropontine terminal fields and the crus IIa projecting neurons. Finally, comparisons with previously published three-dimensional distributions of pontine neurons labelled following tracer injections in face receiving regions in the paramedian lobule (downloaded from http://www.rbwb.org) revealed similar correspondence. The present data support the coherent topographical organization of cerebro-ponto-cerebellar networks previously suggested from physiological studies. We discuss the present findings in the context of transformations from cerebral somatotopic to cerebellar fractured tactile representations.

Animals↗
PubMed2005

Direct adaptive controller for nonaffine nonlinear systems using self-structuring neural networks.

A direct adaptive state-feedback controller is proposed for highly nonlinear systems. We consider uncertain or ill-defined nonaffine nonlinear systems and employ a neural network (NN) with flexible structure, i.e., an online variation of the number of neurons. The NN approximates and adaptively cancels an unknown plant nonlinearity. A control law and adaptive laws for the weights in the hidden layer and output layer of the NN are established so that the whole closed-loop system is stable in the sense of Lyapunov. Moreover, the tracking error is guaranteed to be uniformly asymptotically stable (UAS) rather than uniformly ultimately bounded (UUB) with the aid of an additional robustifying control term. The proposed control algorithm is relatively simple and requires no restrictive conditions on the design constants for the stability. The efficiency of the proposed scheme is shown through the simulation of a simple nonaffine nonlinear system.

Adaptation, Physiological↗
PubMed2002

Cerebral capillary endothelial cell mitogenesis and morphogenesis induced by astrocytic epoxyeicosatrienoic Acid.

UNLABELLED: Background and Purpose- Epoxyeicosatrienoic acids (EETs) are products of cytochrome P450 epoxygenation of arachidonic acid. We have previously demonstrated that astrocyte-conditioned medium induced mitogenesis in brain capillary endothelial cells. The goals of the present studies are to further define the mechanism through which this can occur and to confirm that EETs are derived from astrocytes, through which astrocytic activity can regulate cerebral angiogenesis in response to neuronal activation. METHODS: Astrocytes and cerebral capillary endothelial cells in primary cultures were cocultured to examine the interaction of the 2 cell types. We used multiple immunohistochemical techniques to characterize the multicellular nature of the capillaries, which is not simply an artifact related to the culture conditions. The mitogenic effect of EETs was determined by (3)H-thymidine incorporation and cell proliferation assay. Endothelial tube formation was examined in vitro and in vivo with the use of a reconstituted basement membrane (Matrigel) assay. RESULTS: In cocultures of astrocytes and capillary endothelium, we observed morphological changes in both cell types such that each assumed certain physiological characteristics, ie, endothelial networks and astrocytes with "footlike" projections as well as intermittent gap junctions forming within the endothelial cells. EETs from astrocytes as well as synthetic EETs promoted mitogenesis of endothelial cells, a process sensitive to inhibition of tyrosine kinase with genistein. Treatments with exogenous EETs were sufficient for endothelial cells to differentiate into capillary-like structures in culture as well as in vivo in a Matrigel matrix. CONCLUSIONS: The 2 major conclusions from these data are that astrocytes may play an important role in regulating angiogenesis in the brain and that cytochrome P450-derived EETs from astrocytes are mitogenic and angiogenic.

8,11,14-Eicosatrienoic Acid↗
PubMedDate not supplied

Flexible couplings: diffusing neuromodulators and adaptive robotics.

Recent years have seen the discovery of freely diffusing gaseous neurotransmitters, such as nitric oxide (NO), in biological nervous systems. A type of artificial neural network (ANN) inspired by such gaseous signaling, the GasNet, has previously been shown to be more evolvable than traditional ANNs when used as an artificial nervous system in an evolutionary robotics setting, where evolvability means consistent speed to very good solutions--here, appropriate sensorimotor behavior-generating systems. We present two new versions of the GasNet, which take further inspiration from the properties of neuronal gaseous signaling. The plexus model is inspired by the extraordinary NO-producing cortical plexus structure of neural fibers and the properties of the diffusing NO signal it generates. The receptor model is inspired by the mediating action of eurotransmitter receptors. Both models are shown to significantly further improve evolvability. We describe a series of analyses suggesting that the reasons for the increase in evolvability are related to the flexible loose coupling of distinct signaling mechanisms, one "chemical" and one "electrical."

Adaptation, Physiological↗
PubMed2003

Movement parameters that distinguish between voluntary movements and levodopa-induced dyskinesia in Parkinson's disease.

It is well known that long-term use of levodopa by patients with Parkinson's disease causes dyskinesia. Several methods have been proposed for the automatic, unsupervised detection and classification of levodopa induced dyskinesia. Recently, we have demonstrated that neural networks are highly successful to detect dyskinesia and to distinguish dyskinesia from voluntary movements. The aim of this study was to use the trained neural networks to extract parameters, which are important to distinguish between dyskinesia and voluntary movements. Thirteen patients were continuously monitored in a home-like situation performing in about 35 daily life tasks for a period of approximately 2.5 h. Behavior of the patients was measured using triaxial accelerometers, which were placed at six different positions of the body. A neural network was trained to assess the severity of dyskinesia. The neural network was able to assess the severity of dyskinesia and could distinguish dyskinesia from voluntary movements in daily life. For the trunk and the leg, the important parameters appeared to be the percentage of time that the trunk or leg was moving and the standard deviation of the segment velocity of the less dyskinetic leg. For the arm, the combination of the percentage of time, that the wrist was moving, and the percentage of time, that a patient was sitting, explained the largest part of the variance of the output. Dyskinesia differs from voluntary movements in the fact that dyskinetic movements tend to have lower frequencies than voluntary movements and in the fact that movements of different body segments are not well coordinated in dyskinesia.

Acceleration↗
PubMed2006

Tracking the states of a nonlinear and nonstationary system in the weight-space of artificial neural networks.

We propose a novel interpretation and usage of Neural Network (NN) in modeling physiological signals, which are allowed to be nonlinear and/or nonstationary. The method consists of training a NN for the k-step prediction of a physiological signal, and then examining the connection-weight-space (CWS) of the NN to extract information about the signal generator mechanism. We define a novel feature, Normalized Vector Separation (gamma(ij)), to measure the separation of two arbitrary states "i" and "j" in the CWS and use it to track the state changes of the generating system. The performance of the method is examined via synthetic signals and clinical EEG. Synthetic data indicates that gamma(ij) can track the system down to a SNR of 3.5 dB. Clinical data obtained from three patients undergoing carotid endarterectomy of the brain showed that EEG could be modeled (within a root-means-squared-error of 0.01) by the proposed method, and the blood perfusion state of the brain could be monitored via gamma(ij), with small NNs having no more than 21 connection weight altogether.

Brain Ischemia↗
PubMed2005

Neuromodulation via conditional release of endocannabinoids in the spinal locomotor network.

Endocannabinoids act as retrograde signals to modulate synaptic transmission. Little is known, however, about their significance in integrated network activity underlying motor behavior. We have examined the physiological effects of endocannabinoids in a neuronal network underlying locomotor behavior using the isolated lamprey spinal cord. Our results show that endocannabinoids are released during locomotor activity and participate in setting the baseline burst rate. They are released in response to mGluR1 activation and act as retrograde messengers. This conditional release of endocannabinoids can transform motoneurons and crossing interneurons into modulatory neurons by enabling them to regulate their inhibitory synaptic inputs and thus contribute to the modulation of the locomotor burst frequency. These results provide evidence that endocannabinoid retrograde signaling occurs within the locomotor network and contributes to motor pattern generation and regulation in the spinal cord.

Action Potentials↗
PubMed2005

TGF-beta and epithelial-to-mesenchymal transitions.

Remarkable phenotype plasticity of epithelial cells underlies morphogenesis, epithelial repair and tumor invasiveness. Detailed understanding of the contextual cues and molecular mediators that control epithelial plasticity will be required in order to develop viable therapeutic approaches targeting epithelial-to-mesenchymal transition (EMT), an advanced manifestation of epithelial plasticity. Members of the transforming growth factor (TGF-beta) family of growth factors can initiate and maintain EMT in a variety of biological systems and pathophysiological context by activating major signaling pathways and transcriptional regulators integrated in extensive signaling networks. Here we will review the distinct physiological contexts of EMT and the underlying molecular signaling networks controlled by TGF-beta.

Animals↗
PubMed1998

An expert system for EEG monitoring in the pediatric intensive care unit.

OBJECTIVES: was to design a warning system for the pediatric intensive care unit (PICU). The system should be able to make statements at regular intervals about the level of abnormality of the EEG. The warnings are aimed at alerting an expert that the EEG may be abnormal and needs to be examined. METHODS: A total of 188 EEG sections lasting 6 h each were obtained from 74 patients in the PICU. Features were extracted from these EEGs, and with the use of fuzzy logic and neural networks, we designed an expert system capable of imitating a trained EEGer in providing an overall judgment of abnormality about the EEG. The 188 sections were used in training and testing the system using the rotation method, thus separating training and testing data. RESULTS: The EEGer and the expert system classified the EEGs in 7 levels of abnormality. There was concordance between the two in 45% of cases. The expert system was within one abnormality level of the EEGer in 91% of cases and within two levels in 97%. CONCLUSIONS: We were therefore able to design a system capable of providing reliably an assessment of the level of abnormality of a 6 h section of EEG. This system was validated with a large data set, and could prove useful as a warning device during long-term ICU monitoring to alert a neurophysiologist that an EEG requires attention.

Algorithms↗
PubMed2005

A neural network method for automatic and incremental learning applied to patient-dependent seizure detection.

OBJECTIVE: Describe and evaluate a neural network method for automatic and incremental learning applied to patient-dependent seizure detection. Compare the classification ability of various time-frequency methods including FFT spectrogram, spectral edge frequency and bicoherence. METHODS: 57 seizures from 10 epilepsy patients are used. A probabilistic neural network (PNN) is trained and incrementally updated in a novel fashion. The speed and accuracy of the method is evaluated with different training parameters and time-frequency methods. RESULTS: Training the PNN on a single seizure from each record offers better performance (sensitivity = 0.89 and false-positive-rate = 0.56/h) than 3 patient-independent seizure detection algorithms. The method is virtually unaffected by the settings of various training parameters. Training is very fast (0.9 s), and the accuracy improves as more examples are added incrementally (without retraining). The overall best time-frequency method was the FFT spectrogram. The bicoherence plus the FFT spectrogram was the best method on 4 records, improving the correlation from 0.111 to 0.940 on one and from 0.288 to 0.612 on another. CONCLUSIONS: The proposed method offers accurate, robust and virtually instantaneous training and incremental learning when applied to patient-dependent seizure detection. SIGNIFICANCE: Accurate seizure detection can improve patient care in both the epilepsy monitoring unit and the intensive care unit. Future applications include patient-independent algorithms that continue to learn as new examples are encountered.

Automation↗
PubMed2000

Prognostic significance of advanced neuromonitoring after traumatic brain injury using neural networks.

While the therapeutic impact of tissue oxygenation (PtiO2) supplementing ICP-monitoring is proven by several clinical studies, its prognostic value is not well studied. In the following study artificial neural networks (ANN) were used to analyze the accuracy of outcome prediction after traumatic brain injury (TBI) for different combinations of clinical data and parameters derived from neuromonitoring. The total group included 95 patients suffering from TBI. For all patients clinical data (age, GCS, pupillary response etc.) were recorded and outcome was classified using Glasgow outcome scale after 6 months. In a first step a subgroup of 60 patients was chosen to train a neural network to predict outcome based only on clinical data. In a second step the resting 35 patients all having continuous neuromonitoring with automatic data storage of ICP and PtiO2 were chosen. Different network models were composed using the former clinical model plus up to three additional input units for the following parameters: (a) relative number of ICP > 40 mmHg, (b) relative number of PtiO2 < 5 mmHg and (c) relative number of ICP > 30 mmHg with simultaneous PtiO2 < 10 mmHg. For each model the following time periods were analyzed: day 1-2, day 1-3 and day 1-4 after trauma and additionally day 1-4 after trauma plus last day of neuromonitoring. Pure clinical data allowed to predict outcome with 74.3% accuracy. A combination of clinical data with ICP (a) significantly increased the confidence levels of outcome prediction in all time periods (p < 0.05) with accuracy rates rising up to 82.9% for the longer time periods. The combination of clinical data and ICP & PtiO2 (c) lead to comparable results. In contrast, no significant increases were observed in the early time periods when combining clinical data with PtiO2 (b) while accuracy rates rose up to 80% for extended time periods after trauma. A combination of all parameters lead to results lying between the above results. The results indicate that prediction of outcome can be improved by combining clinical and neuromonitoring data. The prognostic value of ICP might be superior to that of PtiO2.

Brain Injuries↗
PubMed2001

Artificial intelligence: a new approach for prescription and monitoring of hemodialysis therapy.

The effect of dialysis on patients is conventionally predicted using a formal mathematical model. This approach requires many assumptions of the processes involved, and validation of these may be difficult. The validity of dialysis urea modeling using a formal mathematical model has been challenged. Artificial intelligence using neural networks (NNs) has been used to solve complex problems without needing a mathematical model or an understanding of the mechanisms involved. In this study, we applied an NN model to study and predict concentrations of urea during a hemodialysis session. We measured blood concentrations of urea, patient weight, and total urea removal by direct dialysate quantification (DDQ) at 30-minute intervals during the session (in 15 chronic hemodialysis patients). The NN model was trained to recognize the evolution of measured urea concentrations and was subsequently able to predict hemodialysis session time needed to reach a target solute removal index (SRI) in patients not previously studied by the NN model (in another 15 chronic hemodialysis patients). Comparing results of the NN model with the DDQ model, the prediction error was 10.9%, with a not significant difference between predicted total urea nitrogen (UN) removal and measured UN removal by DDQ. NN model predictions of time showed a not significant difference with actual intervals needed to reach the same SRI level at the same patient conditions, except for the prediction of SRI at the first 30-minute interval, which showed a significant difference (P = 0.001). This indicates the sensitivity of the NN model to what is called patient clearance time; the prediction error was 8.3%. From our results, we conclude that artificial intelligence applications in urea kinetics can give an idea of intradialysis profiling according to individual clinical needs. In theory, this approach can be extended easily to other solutes, making the NN model a step forward to achieving artificial-intelligent dialysis control.

Adult↗
PubMed2005

Artificial neural networks are able to recognize gastro-oesophageal reflux disease patients solely on the basis of clinical data.

BACKGROUND: Artificial neural networks (ANN) are modelling mechanisms that are highly flexible and adaptive to solve the non-linearity inherent in the relationship between symptoms and underlying pathology. OBJECTIVES: To assess the efficacy of ANN in achieving a diagnosis of gastro-oesophageal reflux disease (GORD) using oesophagoscopy or pH-metry as a diagnostic gold standard and discriminant analysis as a statistical comparator technique in a group of patients with typical GORD symptoms and with or without GORD objective findings (e.g. a positive oesophagoscopy or a pathological oesophageal pH-metry). METHODS: The sample of 159 cases (88 men, 71 women) presenting with typical symptoms of GORD, were subdivided on the basis of endoscopy and pH-metry results into two groups: GORD patients with or without oesophagitis, group 1 (N=103), and pH and endoscopy-negative patients in whom both examinations were negative, group 2 (N=56). A total of 101 different independent variables were collected: demographic information, medical history, generic health state and lifestyle, intensity and frequency of typical and atypical symptoms based on the Italian version of the Gastroesophageal Reflux Questionnaire (Mayo Clinic). The diagnosis was used as a dependent variable. Different ANN models were assessed. RESULTS: Specific evolutionary algorithms selected 45 independent variables, concerning clinical and demographic features, as predictors of the diagnosis. The highest predictive performance was achieved by a 'back propagation' ANN, which was consistently 100% accurate in identifying the correct diagnosis compared with 78% obtained by traditional discriminant analysis. CONCLUSION: On the basis of this preliminary work, the use of ANN seems to be a promising approach for predicting diagnosis without the need for invasive diagnostic methods in patients suffering from GORD symptoms.

Adult↗
PubMedDate not supplied

Autoimmunity and the immunologic network.

Recent developments in autoimmunity suggest that there are three stages in the response to self, which can be called autorecognition, autoimmunity, and autoimmune disease. The first is physiologic and fundamental to a network theory of immunologic control that is based upon recognition of idiotypes and antigens related to the major histocompatibility complex. Many foreign antigens may be recognized immunologically only if they can be imposed upon an existing network of immunologic communication. The existence of anti-receptor autoimmune diseases (such as myasthenia gravis and Grave's disease) leads to the postulate that the immune network may normally function to help regulate hormone and other nonimmune cell surface receptors. Chronic autoimmune diseases may be caused either by genetically determined abnormalities in the immune network or by an antigenic perturbation of the network that results in unresponsiveness and tolerance of an offending agent.

Animals↗
PubMed2005

The pathophysiology of HOX genes and their role in cancer.

The HOM-C clustered prototype homeobox genes of Drosophila, and their counterparts, the HOX genes in humans, are highly conserved at the genomic level. These master regulators of development continue to be expressed throughout adulthood in various tissues and organs. The physiological and patho-physiological functions of this network of genes are being avidly pursued within the scientific community, but defined roles for them remain elusive. The order of expression of HOX genes within a cluster is co-ordinated during development, so that the 3' genes are expressed more anteriorly and earlier than the 5' genes. Mutations in HOXA13 and HOXD13 are associated with disorders of limb formation such as hand-foot-genital syndrome (HFGS), synpolydactyly (SPD), and brachydactyly. Haematopoietic progenitors express HOX genes in a pattern characteristic of the lineage and stage of differentiation of the cells. In leukaemia, dysregulated HOX gene expression can occur due to chromosomal translocations involving upstream regulators such as the MLL gene, or the fusion of a HOX gene to another gene such as the nucleoporin, NUP98. Recent investigations of HOX gene expression in leukaemia are providing important insights into disease classification and prediction of clinical outcome. Whereas the oncogenic potential of certain HOX genes in leukaemia has already been defined, their role in other neoplasms is currently being studied. Progress has been hampered by the experimental approach used in many studies in which the expression of small subsets of HOX genes was analysed, and complicated by the functional redundancy implicit in the HOX gene system. Attempts to elucidate the function of HOX genes in malignant transformation will be enhanced by a better understanding of their upstream regulators and downstream target genes.

Animals↗
PubMed1997

Of genomes and proteomes.

The era of complete genome sequences has arrived and with it vast amounts of data which must be annotated, cross referenced, and placed within the regulatory networks which define the physiology of an organism. One eucaryotic and three procaryotic genomes have been completed and the data made available and another 50 sequences are expected to be completed by the end of the decade. One of the first steps in the new post genome era will be to decipher the functions of the huge numbers of new open reading frames. Various approaches to investigate what unknown genes do and how genes interact together within an organism are being undertaken, including (1) the simultaneous measurement of the expression levels of all genes in a cell and (2) the mapping and quantitation of all proteins expressed within a cell. The idea of systematically mapping and identifying the total protein complement of the genome (the "proteome") arose over 20 years ago when the separation of proteins from total cell extracts by two dimensional (2D) gel electrophoresis was developed. This review will focus on the use of 2D gel electrophoresis as the basis for constructing proteome maps and on the rapid advances in mass spectrometry which will allow the large-scale, automated identification of proteins which is necessary for the creation of such databases.

Animals↗
PubMed1995

Multiresolution analysis of event-related potentials by wavelet decomposition.

Wavelet analysis is presented as a new tool for analyzing event-related potentials (ERPs). The wavelet transform expands ERPs into a time-scale representation, which allows the analyst to zoom in on the small scale, fine structure details of an ERP or zoom out to examine the large scale, global waveshape. The time-scale representation is closely related to the more familiar time-frequency representation used in spectrograms of time-varying signals. However, time-scale representations have special properties that make them attractive for many ERP applications. In particular, time-scale representations permit theoretically unlimited time resolution for the detection of short-lived peaks and permit a flexible choice of wavelet basis functions for analyzing different types of ERPs. Generally, time-scale representations offer a formal basis for designing new, specialized filters for various ERP applications. Among recently explored applications of wavelet analysis to ERPs are (a) the precise identification of the time of occurrence of overlapping peaks in the auditory brainstem evoked response; (b) the extraction of single-trial ERPs from background EEG noise; (c) the decomposition of averaged ERP waveforms into orthogonal detail functions that isolate the waveform's experimental behavior in distinct, orthogonal frequency bands; and (d) the use of wavelet transform coefficients to concisely extract important information from ERPs that predicts human signal detection performance. In this tutorial we present an intuitive introduction to wavelets and the wavelet transform, concentrating on the multiresolution approach to wavelet analysis of ERP data. We then illustrate this approach with real data. Finally, we offer some speculations on future applications of wavelet analysis to ERP data.

Brain↗
PubMed1998

M-CSF: haematopoietic growth factor or inflammatory cytokine?

Macrophage colony-stimulating factor (M-CSF), initially described as a growth factor of the mononuclear phagocytic lineage, also participates in immunological and inflammatory reactions, bone metabolism and pregnancy. All its biological activities are mediated by a tyrosine kinase receptor (M-CSF-R) that is encoded by the c-fms protooncogene. After a brief overview on the synthesis, structure, metabolism and signalling of M-CSF and its receptor, we present with more details the major in vitro and/or in vivo biological activities of this cytokine. A particular attention has been devoted to the results suggesting that the various M-CSF isoforms (i.e. soluble, cell-associated and matrix anchored forms) play different specific roles on target cells bearing M-CSF-R at their surface. Infectious, inflammatory and neoplastic diseases in which M-CSF is involved and could participate to their physiopathology are mentioned. Finally, the role that the various isoforms of M-CSF could play in the regulation of "physiological and pathological cytokine networks" during inflammatory and immune responses is discussed.

Animals↗